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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781835882702
Length 716 pages
Edition 3rd Edition
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Toc

Table of Contents (29) Chapters Close

Preface 1. Part 1 Introduction to RL FREE CHAPTER
2. What Is Reinforcement Learning? 3. OpenAI Gym API and Gymnasium 4. Deep Learning with PyTorch 5. The Cross-Entropy Method 6. Part 2 Value-based methods
7. Tabular Learning and the Bellman Equation 8. Deep Q-Networks 9. Higher-Level RL Libraries 10. DQN Extensions 11. Ways to Speed Up RL 12. Stocks Trading Using RL 13. Part 3 Policy-based methods
14. Policy Gradients 15. Actor-Critic Method: A2C and A3C 16. The TextWorld Environment 17. Web Navigation 18. Part 4 Advanced RL
19. Continous Action Space 20. Trust Region Methods 21. Black-Box Optimizations in RL 22. Advanced Exploration 23. Reinforcement Learning with Human Feedback 24. AlphaGo Zero and MuZero 25. RL in Discrete Optimization 26. Multi-Agent RL 27. Bibliography
28. Index

Atari experiments

The MountainCar environment is a nice and fast way to experiment with exploration methods, but to conclude the chapter, I’ve included Atari versions of the DQN and PPO methods with the exploration tweaks we described to check a more complicated environment.

As the primary environment, I’ve used Seaquest, which is a game where the submarine needs to shoot fish and enemy submarines, and save aquanauts. This game is not as famous as Montezuma’s Revenge, but it still might be considered as medium-hard exploration because, to continue the game, you need to control the level of oxygen. When it becomes low, the submarine needs to rise to the surface for some time. Without this, the episode will end after 560 steps and with a maximum reward of 20. But once the agent learns how to replenish the oxygen, the game might continue almost infinitely and bring to the agent a 10k-100k score. Surprisingly, traditional exploration methods struggle with...

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